关键词: 生成对抗网络, 卷积神经网络, 故障诊断, 轴承, 少样本学习

Abstract:  Few bearing samples usually led to inadequate learning and low diagnosis accuracy. To solve this problem， an improved convolutional generative adversarial network was constructed. It made full use of data generation ability of the GAN and learning ability of deep CNN， so that the intelligent fault diagnosis might be conducted for the bearings with few samples under varying working conditions. First， a deep convolutional GAN was constructed. The deep features in few real data were mined through adversarial learning between the generator and discriminator of GAN， and then the generator might generate simulated data exactly like real one to make up for the lack of very few samples. Then， the dense block and dilated convolutions were combined with the CNN， named as DDCNN， to improve the learning ability by extending the network depth and perception range. As a result， the DDCNN may identify tiny differences in multi-class datasets and enhance feature extraction. Finally， the proposed method was verified by using bearing datasets with few samples under fixed conditions and varying rotating speeds， and was compared with other frameworks. The experimental results indicate that DDCNN has higher diagnosis accuracy for bearings with few samples and noisy conditions. 

Key words: generative adversarial network（GAN）, convolutional neural network（CNC）, fault diagnosis, bearing, few-shot learning